Turning boxes into supportive circles: Enhancing online group work teaching during the COVID-19 pandemic
Bibliographic record
Abstract
This paper reflects the collective experiences of fourteen internationally based social group work educators who met weekly and virtually for seven months during the transition to online teaching necessitated by the coronavirus pandemic. The meetings functioned as a mutual aid support group sponsored by the International Association of Social Work with Groups (IASWG). The paper discusses the group’s perceptions of the essential components of effective online group work education. It begins with a review of the history of online social work education. It then outlines the key components instrumental in the planning and developing of engaged online group work classes. Topics include pre-course preparation, norm setting, and building community in the online classroom. Considerations related to the video conferencing platforms, course formats, activities, managing online fatigue, screen sharing, handling chat features, cameras, and break out rooms are interspersed throughout. The paper concludes with a discussion of the use of mutual aid groups as online teaching tools and highlights the online group work teaching experiences of two members in New Zealand and Namibia. Despite initial hesitancy to teach group work virtually, the authors recognize that this can be done effectively but requires additional planning and, ideally, peer and institutional support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".